EI: Early Intervention for Multimodal Imaging based Disease Recognition
This work addresses multimodal medical imaging challenges for disease diagnosis, representing an incremental improvement with novel architectural components.
The paper tackles the problem of multimodal medical imaging disease recognition by addressing limitations in existing fusion paradigms and adapting Vision Foundation Models to medical domains, achieving state-of-the-art results on three public datasets for retinal disease, skin lesion, and knee anomaly classification.
Current methods for multimodal medical imaging based disease recognition face two major challenges. First, the prevailing "fusion after unimodal image embedding" paradigm cannot fully leverage the complementary and correlated information in the multimodal data. Second, the scarcity of labeled multimodal medical images, coupled with their significant domain shift from natural images, hinders the use of cutting-edge Vision Foundation Models (VFMs) for medical image embedding. To jointly address the challenges, we propose a novel Early Intervention (EI) framework. Treating one modality as target and the rest as reference, EI harnesses high-level semantic tokens from the reference as intervention tokens to steer the target modality's embedding process at an early stage. Furthermore, we introduce Mixture of Low-varied-Ranks Adaptation (MoR), a parameter-efficient fine-tuning method that employs a set of low-rank adapters with varied ranks and a weight-relaxed router for VFM adaptation. Extensive experiments on three public datasets for retinal disease, skin lesion, and keen anomaly classification verify the effectiveness of the proposed method against a number of competitive baselines.